• Journal of Medicine and Life Science
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• 제20권2호
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• pp.73-82
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• 2023

Multiple studies have reported on unilateral axillary adenopathy following coronavirus disease 2019 (COVID-19) vaccination, which is currently recognized as a common finding. Here, we present a series of eight adult patients with reactive axillary lymphadenopathy following COVID-19 vaccination, in whom the follow-up ultrasonography (US) showed resolution of a previously noted unilateral axillary adenopathy. From March 2021 to March 2022, 2,599 consecutive women underwent breast US in Jeju National University Hospital. We identified 10 patients with unilateral axillary lymphadenopathy following COVID-19 vaccination detected on the breast US. The 10 patients were recommended for follow-up US. Two patients were lost to follow-up, whereas the remaining eight patients underwent follow-up US, in whom resolution of the unilateral axillary lymphadenopathy was noted. Radiologists should be aware of evolving guidelines for evaluating and managing axillary lymphadenopathy to avoid false positive biopsies. Recent studies on lymphadenopathy following COVID-19 vaccination show that a prolonged duration until resolution is often observed. Therefore, a follow-up US examination at least 12 weeks after vaccination may be reasonable. Furthermore, management guidelines should include a risk-stratified approach considering both vaccination timing and the patient's overall risk of metastatic disease.

• Radiation Oncology Journal
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• 제22권2호
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• pp.115-123
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• 2004

목적 : 조기 유방암에서 유방보존치료(유방보존수술+방사선치료) 혹은 유방전절제술로 치료했던 환자의 초기 치료성적과 실패양상을 비교하고자 후향적으로 분석하였다. 대상 및 방법 : 1989년 3월부터 1996년 8월까지 강남성모병원에서 AJCC병기 I, II로 치료를 받았던 유방암 환자를 대상으로 하였다. 88명은 유방전절제술을 시행하였고 85명은 유방보존술 후 방사선치료를 시행하였다. 방사선치료는 50 Gy 전 유방조사 후 원발부위에 10$\~$15 Gy 추가 조사하였다. 유방보존치료 환자의 34.1%$\%$ 유방전절제 환자의 45.5$\%$에서 항암화학요법이 병용되었다 양 치료군의 5년생존율과 5년무병생존율, 실패양상을 비교하였으며 치료실패와 연관된 위험인자를 Log-rank test를 이용하여 분석하였다. 중앙 추적기간은 63개월이 었다. 결과 : 양 치료군 간에 5년생존율, 5년무병생존율의 유의한 차이(p>0.05)는 없었으며 국소재발 및 원격전이의 치료실패양상에도 차이가 없었다. 추적기간 중, 유방전절제군에서 11명(12.5$\%$) 유방보존치료군에서 10명(11.8$\%$) 재발하였다. 초기 실패양상은 국소재발이 각각 6명, 5명이었고 원격전이가 각각 5명, 4명으로 차이가 없었다. 국소재발 단독의 경우 양 치료군에서 구제치료 후 대부분의 환자가 무병생존 (5/6 유방전절제술, 3/5 유방보존치료)하였다 그러나 원격전이 환자의 경우 양 치료군 모두에서 방사선-항암화학요법의 구제치료에도 불구하고 대부분의 환자가 진행 혹은 사망하였으며 유방보존치료군의 1명의 환자만이 원격전이 후 구제치료에 성공하여 무병생존하였다. 양 치료군 간에 반대편유방암 발생률 및 다른 장기의 2차 원발암 발생률의 차이는 없었고 유방암으로 인한 사망률도 차이가 없었다. Log-rank 단변량분석에서 치료 실패와 관련된 유의한 위험인자는 양 군 모두에서 N 병기, 액와 림프절 전이 숫자였으며 유방보존치료군에서는 수술절연침범유무가, 유방전절제군에서는 high nuclear grade가 치료실패와 관련된 위험인자였다(p<0.05). 결론 : 분석결과 AJCC 병기I, II 조기유방암에서 유방보존치료와 유방전절제술은 생존율뿐 아니라 치료 실패양상에도 차이가 없었으며 향후 이와 같은 결과를 확인하기 위한 장기간의 추적연구가 필요하다.

• Asian Pacific Journal of Cancer Prevention
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• 제15권6호
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• pp.2893-2900
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• 2014

Background: Statistical methods are very important to precisely measure breast cancer patient survival times for healthcare management. Previous studies considered basic statistics to measure survival times without incorporating statistical modeling strategies. The objective of this study was to develop a data-based statistical probability model from the female breast cancer patients' survival times by using the Bayesian approach to predict future inferences of survival times. Materials and Methods: A random sample of 500 female patients was selected from the Surveillance Epidemiology and End Results cancer registry database. For goodness of fit, the standard model building criteria were used. The Bayesian approach is used to obtain the predictive survival times from the data-based Exponentiated Exponential Model. Markov Chain Monte Carlo method was used to obtain the summary results for predictive inference. Results: The highest number of female breast cancer patients was found in California and the lowest in New Mexico. The majority of them were married. The mean (SD) age at diagnosis (in years) was 60.92 (14.92). The mean (SD) survival time (in months) for female patients was 90.33 (83.10). The Exponentiated Exponential Model found better fits for the female survival times compared to the Exponentiated Weibull Model. The Bayesian method is used to obtain predictive inference for future survival times. Conclusions: The findings with the proposed modeling strategy will assist healthcare researchers and providers to precisely predict future survival estimates as the recent growing challenges of analyzing healthcare data have created new demand for model-based survival estimates. The application of Bayesian will produce precise estimates of future survival times.

• Asian Pacific Journal of Cancer Prevention
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• 제13권3호
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• pp.979-983
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• 2012

Background : Previous studies have suggested a lack of complete cross-resistance between steroidal (exemestane) and non-steroidal aromatase inhibitors (nSAI). Methods : Eighty-eight metastatic breast cancer (MBC) patients who received 25 mg of exemestane orally once a day at the National Cancer Center, Korea, between 2003 and 2009, were reviewed retrospectively. All patients had received nSAI for metastatic disease prior to exemestane therapy. Results : The median age was 52 years (range, 33-79), and 13 (14.8%) patients were premenopausal who concomitantly received GnRH agonist. Exemestane was given as a second- (80.7%) or third-line (19.3%) hormone therapy. The clinical benefit (CB) rate (complete response + partial response + stable disease ${\geq}$ 24 weeks) was 30.7%, with a median CB duration of 10.0 months (range, 6.3-78.7). The median progression-free survival (PFS) was 3.0 months (95% confidence interval [CI], 1.99-4.01) and the overall survival (OS) 21.5 months (95% CI, 17.96-25.04), with a median followup of 50.3 months. Patients who achieved CB had longer OS than those patients who did not (29.6 vs 17.9 months; P=0.002). On univariate analysis of predictive factors, patients who had achieved CB from previous nSAI tended to show lower CB rate (24.6% vs 44.4%, respectively; P=0.063) and shorter PFS (2.8 vs 4.8 months, respectively; p=0.233) than patients who had not. Achieving CB from previous nSAI became independent predictive factor for CBR to exemestane on multivariable analysis (Odds ratio = 2.852, P = 0.040). Conclusions : Exemestane after nSAI failure was effective in prolonging CB duration. The drug's efficacy seemed to be inferior in patients who had benefit from previous nSAI use.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• 대한세포병리학회지
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• 제7권1호
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• pp.12-22
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• 1996

세침흡인 검사는 촉지되거나 혹은 촉지되지 않는 양성과 악성 유방 질환을 인지하는 매우 유용한 방법이나 진단이 애매한 회색 지역이 존재한다. 1년동안 시행한 697예의 유방 세침흡인 검사 중 111예가 비정형적 혹은 악성이 의심된 다고 보고되었으며 이 중 조직학적으로 확진된 74예를 재검색하여 Masood 등 (1990)이 제안한 등급 점수표가 회색 지역을 줄이는데 유용한지 살펴보았다. 기술적 문제가 19예에서 나타났으며 나머지 55예 중 18예는 양성으로, 37예는 악성으로 조직학적으로 진단되었다. 양성 질환 중 섬유선종(5예)및 섬유선종의 성상을 지닌 섬유낭성 질환(3예)이 가장 많았고 악성은 대부분 침윤성 관암종이였으며 (29예), 관상암종(3예), 사상암종(2예) 및 점액 암종(2예) 등 분화가 좋은 암종들이 상대적으로 많았다. 등급 점수표는 회색 지역을 줄이는데 상당히 유용하여 29예의 관암종중 27예가 세포학적 악성 범주에 속했다. 그러나 분화가 좋은 암종은 7예중 2예만이 세포학적 악성 범주에 속했고 섬유선종 및 섬유선종의 성상을 지닌 섬유낭성 질환은 비교적 높은 점수를 보였다. 따라서 등급 점수표의 응용은 유방 세침흡인 검사에서 양성과 악성 병변의 감별에 매우 유용하게 이용될 수 있으나 분화가 좋은 암종들과 섬유선종의 진단에는 주의가 필요할 것으로 사료되며 유방 세침흡인 세포학적 검사에는 세포 병리 의사의 경험 및 유방질환의 세포학적 변조에 대한 친숙성이 강조되어야 할 것이다.

• Asian Pacific Journal of Cancer Prevention
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• 제14권12호
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• pp.7445-7449
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• 2013

Background: Breast cancer (BC) is the one of the most common cancers in women. It is also a leading cause of death. Unfortunately, some patients initially present with distant metastases and are diagnosed with stage IV disease that is nearly always, by then, incurable. This retrospective analysis investigated the risk factors for stage IV BC that may underlie such late presentation. Materials and Methods: In all, 916 patients with BC who visited the medical oncology polyclinic of eight different centres in Turkeybetween December 2011 and January 2013 were analysed. Results: A total of 115 patients (12.6%) presented with stage IV disease. In univariate analysis; to comparing these with patients at other stages, no statistical difference was found for median diagnosis age or age at menarche (p=0.611 and p=0.820), whereas age at menopause and age at first live birth were significant (p=0.018 and p=0.003). No difference was detected in terms of accompanying diseases, use of oral contraceptives and hormone replacement therapy, smoking, alcohol consumption and the rate of family history of BC between the patients (p=0.655, p=0.389, p=0.762, p=0.813, p=0.229, p=0.737). However, screening methods were employed less often, the rate of illiteracy was higher, and the rate of other cancers was higher in patients with stage IV BC (p=0.022, p=0.022, p=0.018). No statistical difference was observed between the patients in terms of tumour histopathology, and status of oestrogen receptor, progesterone receptor, or human epidermal growth factor-2 receptor (p=0.389, p=0.326, p=0.949, p=0.326). Grade 3 tumours were more frequent in patients with stage IV disease (p<0.001). On multivariate analysis, risk factors for stage IV breast cancer at the time of presentation were found to be age at first live birth and educational level (p=0.003 and p=0.047). Conclusions: Efforts should be made to perform mammography scans, in particular, at regular intervals through national training programs for all women, particularly those with family histories of breast and other types of cancer, and to establish early diagnosis of BC long before it proceeds to stage IV. Additionally, women's education had better be upgraded. In order to make women aware of BC, national education-programmes must be organised.

• Asian Pacific Journal of Cancer Prevention
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• 제14권1호
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• pp.413-417
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• 2013

Purpose: This phase II study was undertaken to determine the efficacy and safety of Loubo$^{(R)}$ (Lobaplatin) in combination with pemetrexed in treating patients with metastatic breast cancer who failed to respond to anthracycline or taxanes. Patients and Methods: Metastatic breast cancer cases who had previously received an anthracycline and a taxane in either adjuvant or metastatic settings, were enrolled. All patients were recruited from Jiangsu Cancer Hospital and Research Institute, and were treated with Loubo$^{(R)}$ (Lobaplatin) 35 $mg/m^2$ (intravenous; on day 1) and pemetrexed 500 $mg/m^2$ (intravenous; on day 1) every 21 days. Efficacy and side effects were evaluated after at least two cycles of chemotherapy. Results: All eligible 19 patients completed at least 2 cycles of chemotherapy with pemetrexed and lobaplatin, and were evaluable. Overall, 3 (15.8%) patients achieved partial response, 11 (57.9%) stable disease, 5 (26.3%) progression of disease, with no complete remission. Response rate was 15.8%, disease control rate was 42.1%. The median survival time was 10.3 months. Neutrophil suppression occurred in 36.8% of patients who had grade 2 toxicity, and 26.3% had grade 3, 26.4% had grade 4. Thrombocytopenia was encountered as follows: 21.1% grade 2, 15.8% grade 3 and 5.5% grade 4. Incidences of anemia were 10.5% in grade 2, 5.3% grade 3 and 0% grade 4. Only 5.3% of patients required packed red blood cell transfusion. Grade 3 digestive tract toxicity occurred in 5.5% of patients. Other toxicities included elevated transaminase,oral mucositis and skin rashes. Conclusions: The regimen of lobaplatin and pemetrexed is modestly active in metastatic breast cancer patients who failed anthracycline or taxanes, and the toxicity profile suggesting that the doses of chemotherapy should be further modified.

• Asian Pacific Journal of Cancer Prevention
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• 제15권24호
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• pp.10577-10583
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• 2015

Background: Triple-negative breast cancer (TNBC), characterized by the lack of expression of estrogen receptor, progesterone receptor and human epidermal growth factor receptor-2, is typically associated with a poor prognosis. The majority of TNBCs show the expression of basal markers on gene expression profiling and most authors accept TNBC as basal-like (BL) breast cancer. However, a smaller fraction lacks a BL phenotype despite being TNBC. The literature is silent on non-basal-like (NBL) type of TNBC. The present study was aimed at defining behavioral differences between BL and NBL phenotypes. Objectives: i) Identify the TNBCs and categorize them into BL and NBL breast cancer. ii) Examine the behavioral differences between two subtypes. iii) Observe the pattern of treatment failure among TNBCs. Materials and Methods: All TNBC cases during January 2009-December 2010 were retrieved. The subjects fitting the inclusion criteria of study were differentiated into BL and NBL phenotypes using surrogate immunohistochemistry with three basal markers $34{\beta}E12$, c-Kit and EGFR as per the algorithm defined by Nielsen et al. The detailed data of subjects were collated from clinical records. The comparison of clinicopathological features between two subgroups was done using statistical analyses. The pattern of treatment failure along with its association with prognostic factors was assessed. Results: TNBC constituted 18% of breast cancer cases considered in the study. The BL and NBL subtypes accounted for 81% and 19% respectively of the TNBC group. No statistically significant association was seen between prognostic parameters and two phenotypes. Among patients with treatment failure, 19% were with BL and 15% were with NBL phenotype. The mean disease free survival (DFS) in groups BL and NBL was 30.0 and 37.9 months respectively, while mean overall survival (OS) was 31.93 and 38.5 months respectively. Treatment failure was significantly associated with stage (p=.023) among prognostic factors. Conclusions: Disease stage at presentation is an important prognostic factor influencing the treatment failure and survival among TNBCs. Increasing tumor size is related to lymph node positivity. BL tumors have a more aggressive clinical course than that of NBL as shown by shorter DFS and OS, despite having no statistically significant difference between prognostic parameters. New therapeutic alternatives should be explored for patients with this subtype of breast cancer.

• Asian Pacific Journal of Cancer Prevention
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• 제15권15호
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• pp.6109-6113
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• 2014

Background: Breast cancer is a heterogeneous disease with molecular subtypes that have biological distinctness and different behavior. They are classified into luminal A, luminal B, Her-2 and triple negative/basal-like molecular subtypes. Most of breast cancers reported in Indonesia are already large size, with high grade or late stage but the clinicopathological features of different molecular subtypes are still unclear. They need to be better clarified to determine proper treatment and prognosis. Aim: To elaborate the clinicopathological features of molecular subtypes of breast cancers in Indonesian women. Materials and Methods: A retrospective cross-sectional study of 84 paraffin-embedded tissues of breast cancer samples from Dr. Sardjito General Hospital in Central Java, Indonesia was performed. Expression of ER, PR, Her-2 and Ki-67 was analyzed to classify molecular subtypes of breast cancer by immunohistochemistry. The relation of clinicopathological features of breast cancers with molecular subtypes of luminal A, luminal B, Her-2 and triple negative/basal-like were analyzed using Pearson's Chi-Square test. A p-value of <0.05 was considered statistically significant. Results: Case frequency of luminal A, Luminal B, Her-2+ and triple negative/basal-like subtypes were 38.1%, 16.7%, 20.2% and 25%, respectively. Significant difference was found in breast cancer molecular subtypes in regard to age, histological grade, lymph node status and staging. However it showed insignificant result in regard to tumor size. Luminal A subtype of breast cancer was commonly found in >50 years old women (p:0.028), low grade cancer (p:0.09), negative lymph node metastasis (p:0.034) and stage III (p:0.017). Eventhough the difference was insignificant, luminal A subtype breast cancer was mostly found in small size breast cancer (p:0.129). Her-2+ subtype breast cancer was more commonly diagnosed with large size, positive lymph node metastasis and poor grade. Triple negative/basal-like cancer was mostly diagnosed among <50 years old women. Conclusions: This study suggests that immunohistochemistry-based subtyping is essential to classify breast carcinoma into subtypes that vary in clinicopathological features, implying different therapeutic options and prognosis for each subtype.